code: def _load_expert_models(
      scenario_name, base_model, run_id, len_stream
  ):
      """Load ExML experts.
  
      If necessary, the model are automatically downloaded.
      """
      # base_dir = f'/raid/carta/EXML_CLVISION_PRETRAINED_EXPERTS/{scenario_name}'
      base_dir = default_dataset_location(
          f"EXML_CLVISION22_PRETRAINED_EXPERTS/{scenario_name}/run{run_id}"
      )
  
      weburl = (
          f"http://131.114.50.174/data/EXML_CLVISION22_PRETRAINED_EXPERTS"
          f"/{scenario_name}/run{run_id}"
      )
  
      experts_stream = []
      for i in range(len_stream):
          fname_i = f"{base_dir}/model_e{i}.pth"
          weburl_i = f"{weburl}/model_e{i}.pth"
  
          if not os.path.exists(fname_i):
              os.makedirs(base_dir, exist_ok=True)
              print(f"Downloading expert model {i}")
              urllib.request.urlretrieve(weburl_i, fname_i)
  
          model = copy.deepcopy(base_model)
          state_d = torch.load(fname_i)
          model.load_state_dict(state_d)
          model.to("cpu").eval()
          experts_stream.append(model)
      return experts_stream
  
imports: import urllib
  import os
  import torch
  from torch import nn
  from torch.utils.data import DataLoader
  from torchvision.models import mobilenet_v2
  from torchvision.transforms import RandomHorizontalFlip, RandomCrop, RandomRotation, ToTensor, CenterCrop, Normalize, Resize
  from avalanche.benchmarks.classic.ccifar10 import SplitCIFAR10
  from avalanche.benchmarks.classic.cmnist import SplitMNIST
  from avalanche.benchmarks.classic.core50 import CORe50
  from avalanche.benchmarks.datasets.dataset_utils import default_dataset_location
  from avalanche.benchmarks.utils.utils import concat_datasets
  from avalanche.models import LeNet5, SlimResNet18
  from torchvision.transforms import Compose
  from avalanche.evaluation.metrics import TaskAwareAccuracy
  from avalanche.benchmarks import ExModelCLScenario, nc_benchmark
  import copy
  import urllib.request
label_desc: Use image node_id 1 for calling a global function with example usage: _load_expert_models(scenario_name, base_model, run_id, len_stream) and returns: experts_stream